



'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BeitImageProcessor

class 				a__(   unittest.TestCase      ):







    def __init__( self					:    Any		,     __snake_case					:    str		,     __snake_case					:    List[str]=7		,     __snake_case					:    List[str]=3		,     __snake_case					:    Any=18		,     __snake_case					:    Union[str, Any]=30		,     __snake_case					:    List[str]=4_00		,     __snake_case					:    List[str]=True		,     __snake_case					:    List[str]=None		,     __snake_case					:    List[str]=True		,     __snake_case					:    List[str]=None		,     __snake_case					:    List[str]=True		,     __snake_case					:    str=[0.5, 0.5, 0.5]		,     __snake_case					:    Optional[int]=[0.5, 0.5, 0.5]		,     __snake_case					:    Any=False		,     ):
      a		:   Dict	  =	size if size is not None else {'height': 20, 'width': 20}
      a		:   Optional[int]	  =	crop_size if crop_size is not None else {'height': 18, 'width': 18}
      a		:   List[Any]	  =	parent
      a		:   Dict	  =	batch_size
      a		:   str	  =	num_channels
      a		:   str	  =	image_size
      a		:   str	  =	min_resolution
      a		:   Dict	  =	max_resolution
      a		:   List[str]	  =	do_resize
      a		:   Optional[Any]	  =	size
      a		:   Optional[int]	  =	do_center_crop
      a		:   str	  =	crop_size
      a		:   Tuple	  =	do_normalize
      a		:   str	  =	image_mean
      a		:   Union[str, Any]	  =	image_std
      a		:   Optional[Any]	  =	do_reduce_labels







    def      lowercase_			( self					:    Dict      ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }
def  lowerCamelCase__			(		):
  a		:   List[Any]	  =	load_dataset('hf-internal-testing/fixtures_ade20k'					,     split='test'							)

  a		:   Union[str, Any]	  =	Image.open(dataset[0]['file']							)
  a		:   str	  =	Image.open(dataset[1]['file']							)

  return image, map
def  lowerCamelCase__			(		):
  a		:   Optional[int]	  =	load_dataset('hf-internal-testing/fixtures_ade20k'					,     split='test'							)

  a		:   Dict	  =	Image.open(ds[0]['file']							)
  a		:   Union[str, Any]	  =	Image.open(ds[1]['file']							)
  a		:   Union[str, Any]	  =	Image.open(ds[2]['file']							)
  a		:   Any	  =	Image.open(ds[3]['file']							)

  return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       BeitImageProcessor if is_vision_available() else None







    def      lowercase_			( self					:    Any      ):
      a		:   List[str]	  =	BeitImageProcessingTester(self      )







    @property
    def      lowercase_			( self					:    str      ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      lowercase_			( self					:    List[Any]      ):
      a		:   int	  =	self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(__snake_case		,     'do_resize'      )      )
      self.assertTrue(hasattr(__snake_case		,     'size'      )      )
      self.assertTrue(hasattr(__snake_case		,     'do_center_crop'      )      )
      self.assertTrue(hasattr(__snake_case		,     'center_crop'      )      )
      self.assertTrue(hasattr(__snake_case		,     'do_normalize'      )      )
      self.assertTrue(hasattr(__snake_case		,     'image_mean'      )      )
      self.assertTrue(hasattr(__snake_case		,     'image_std'      )      )







    def      lowercase_			( self					:    int      ):
      a		:   Union[str, Any]	  =	self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size		,     {'height': 20, 'width': 20}      )
      self.assertEqual(image_processor.crop_size		,     {'height': 18, 'width': 18}      )
      self.assertEqual(image_processor.do_reduce_labels		,     __snake_case      )

      a		:   int	  =	self.image_processing_class.from_dict(
          self.image_processor_dict		,     size=42		,     crop_size=84		,     reduce_labels=__snake_case      )
      self.assertEqual(image_processor.size		,     {'height': 42, 'width': 42}      )
      self.assertEqual(image_processor.crop_size		,     {'height': 84, 'width': 84}      )
      self.assertEqual(image_processor.do_reduce_labels		,     __snake_case      )







    def      lowercase_			( self					:    str      ):
      pass







    def      lowercase_			( self					:    List[Any]      ):
      # Initialize image_processing
      a		:   Dict	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      a		:   str	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     Image.Image      )

      # Test not batched input
      a		:   Dict	  =	image_processing(image_inputs[0]		,     return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape		,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )

      # Test batched
      a		:   List[Any]	  =	image_processing(__snake_case		,     return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )







    def      lowercase_			( self					:    Any      ):
      # Initialize image_processing
      a		:   str	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      a		:   List[Any]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     numpify=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     np.ndarray      )

      # Test not batched input
      a		:   Optional[Any]	  =	image_processing(image_inputs[0]		,     return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape		,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )

      # Test batched
      a		:   Tuple	  =	image_processing(__snake_case		,     return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )







    def      lowercase_			( self					:    Any      ):
      # Initialize image_processing
      a		:   Union[str, Any]	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      a		:   List[str]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     torchify=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     torch.Tensor      )

      # Test not batched input
      a		:   Tuple	  =	image_processing(image_inputs[0]		,     return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape		,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )

      # Test batched
      a		:   int	  =	image_processing(__snake_case		,     return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )







    def      lowercase_			( self					:    List[str]      ):
      # Initialize image_processing
      a		:   Union[str, Any]	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      a		:   Optional[Any]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     torchify=__snake_case      )
      a		:   int	  =	[]
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     torch.Tensor      )
        maps.append(torch.zeros(image.shape[-2:]      ).long()      )

      # Test not batched input
      a		:   int	  =	image_processing(image_inputs[0]		,     maps[0]		,     return_tensors='pt'      )
      self.assertEqual(
          encoding['pixel_values'].shape		,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(
          encoding['labels'].shape		,     (
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(encoding['labels'].dtype		,     torch.long      )
      self.assertTrue(encoding['labels'].min().item() >= 0      )
      self.assertTrue(encoding['labels'].max().item() <= 2_55      )

      # Test batched
      a		:   List[Any]	  =	image_processing(__snake_case		,     __snake_case		,     return_tensors='pt'      )
      self.assertEqual(
          encoding['pixel_values'].shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(
          encoding['labels'].shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(encoding['labels'].dtype		,     torch.long      )
      self.assertTrue(encoding['labels'].min().item() >= 0      )
      self.assertTrue(encoding['labels'].max().item() <= 2_55      )

      # Test not batched input (PIL images)
      a  ,      a		:   Optional[Any]	  =	prepare_semantic_single_inputs()

      a		:   List[str]	  =	image_processing(__snake_case		,     __snake_case		,     return_tensors='pt'      )
      self.assertEqual(
          encoding['pixel_values'].shape		,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(
          encoding['labels'].shape		,     (
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(encoding['labels'].dtype		,     torch.long      )
      self.assertTrue(encoding['labels'].min().item() >= 0      )
      self.assertTrue(encoding['labels'].max().item() <= 2_55      )

      # Test batched input (PIL images)
      a  ,      a		:   Tuple	  =	prepare_semantic_batch_inputs()

      a		:   Optional[Any]	  =	image_processing(__snake_case		,     __snake_case		,     return_tensors='pt'      )
      self.assertEqual(
          encoding['pixel_values'].shape		,     (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(
          encoding['labels'].shape		,     (
              2,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )		,     )
      self.assertEqual(encoding['labels'].dtype		,     torch.long      )
      self.assertTrue(encoding['labels'].min().item() >= 0      )
      self.assertTrue(encoding['labels'].max().item() <= 2_55      )







    def      lowercase_			( self					:    List[Any]      ):
      # Initialize image_processing
      a		:   str	  =	self.image_processing_class(**self.image_processor_dict      )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      a  ,      a		:   Dict	  =	prepare_semantic_single_inputs()
      a		:   Tuple	  =	image_processing(__snake_case		,     __snake_case		,     return_tensors='pt'      )
      self.assertTrue(encoding['labels'].min().item() >= 0      )
      self.assertTrue(encoding['labels'].max().item() <= 1_50      )

      a		:   Tuple	  =	True
      a		:   Tuple	  =	image_processing(__snake_case		,     __snake_case		,     return_tensors='pt'      )
      self.assertTrue(encoding['labels'].min().item() >= 0      )
      self.assertTrue(encoding['labels'].max().item() <= 2_55      )




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """bert"""







    def __init__( self					:    List[str]		,     __snake_case					:    Optional[Any]=3_05_22		,     __snake_case					:    Any=7_68		,     __snake_case					:    str=12		,     __snake_case					:    Optional[int]=12		,     __snake_case					:    Tuple=30_72		,     __snake_case					:    Optional[Any]="gelu"		,     __snake_case					:    Dict=0.1		,     __snake_case					:    Optional[int]=0.1		,     __snake_case					:    List[Any]=5_12		,     __snake_case					:    Optional[Any]=2		,     __snake_case					:    Optional[Any]=0.02		,     __snake_case					:    List[str]=1e-1_2		,     __snake_case					:    Dict=0		,     __snake_case					:    List[str]="absolute"		,     __snake_case					:    List[Any]=True		,     __snake_case					:    List[str]=None		,     **__snake_case					:    Any		,     ):
      super().__init__(pad_token_id=__snake_case		,     **__snake_case      )

      a		:   str	  =	vocab_size
      a		:   List[Any]	  =	hidden_size
      a		:   Union[str, Any]	  =	num_hidden_layers
      a		:   List[Any]	  =	num_attention_heads
      a		:   str	  =	hidden_act
      a		:   str	  =	intermediate_size
      a		:   Tuple	  =	hidden_dropout_prob
      a		:   Union[str, Any]	  =	attention_probs_dropout_prob
      a		:   List[Any]	  =	max_position_embeddings
      a		:   Union[str, Any]	  =	type_vocab_size
      a		:   List[Any]	  =	initializer_range
      a		:   Dict	  =	layer_norm_eps
      a		:   Any	  =	position_embedding_type
      a		:   List[str]	  =	use_cache
      a		:   List[str]	  =	classifier_dropout

class 				a__(   lowerCamelCase__      ):







    @property
    def      lowercase_			( self					:    List[str]      ):
      if self.task == "multiple-choice":
        a		:   Tuple	  =	{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
        a		:   List[Any]	  =	{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
              ('token_type_ids', dynamic_axis),
          ]      )